Accelerate Internship Opportunities

Preferred Disciplines: Machine Learning, Computer Science, Computer Engineering, Operations Research, Data Science (Masters, PhD and Post-Doc)Company: QindomProject Length: 1 yearDesired start date: As soon as possibleLocation: Toronto, ONNo. of Positions: 2-3 (1-2 PhD/Post-Doc and 1-2 master-level intern)Preferences: We would prefer candidates based in Ontario, especially GTA. We also prefer students from University of Toronto and University of Waterloo.

About the Company:

Qindom is a premier Quantum Intelligence (QI) research and application service provider. Born as the game-changer in the present AI world, we focus on developing Quantum Machine Learning (QML) algorithms and addressing complex AI optimization problems. Qindom has gathered the most brilliant minds of our times from different academics and industries. We do not just believe that the QI era is here, we practice realizing that. With our proprietary QML algorithms, we apply quantum-inspired and quantum-classic hybridization principles to fill the gap between users’ AI demands and realization via Quantum Intelligence as a Service provided by our Quantum Intelligence Toolbox.

Project Description:

Project 1

The project will be focused on developing a new quantum machine learning (QML) algorithm. We expect to first design and implement the architecture that links quantum optimization with classical machine learning methods (including deep learning and neural network), then build our novel models on top of the new architecture.

Project 2

The project is set to address sequencial location-based combinatorial optimization in an accurate and effective manner. Quantum machine learning and quantum optimiztion alogorithms will be applied through an API built for Qindom’s ISV partners in the logistics planning market.

Research Objectives:​

Project 1

Objective: The new algorithm is projected to boost the training and learning capabilities of classical machine learning (including deep learning and neural network) algorithms both in terms of speed and accuracy.

Sub-objectives:

Formalize the theories and proofs for the new quantum machine learning algorithm and implement it in our hybrid architecture;